Create minimum spanning networks interactively

Description

This function will launch an interactive interface that allows you to create,
plot, manipulate, and save minimum spanning networks. It runs using the
shiny R package.

Usage

1

imsn()

Details

Creating and plotting MSNs requires three steps:

Create a distance matrix from your data

Create a minimum spanning network with your data and the matrix

Visualize the minimum spanning network

The function plot_poppr_msn is currently the most flexible way
of visualizing your minimum spanning network, but with 20 parameters, it can
become pretty intimidating trying to find the right display for your MSN.

With this function, all three steps are combined into one interactive
interface that will allow you to intuitively modify your minimum spanning
network and even save the results to a pdf or png file.

Value

NULL, invisibly

Interface

Buttons

In the left hand panel, there are three buttons to execute the functions.
These allow you to run the data set after you manipulate all of the
parameters.

GO! - This button will start the application with the
specified parameters

reData - Use this button when you have changed any parameters
under the section Data Parameters. This involves recalculating the
distance matrix and msn.

reGraph - Use this button when you have changed any parameters
under the section Graphical Parameters. This involves superficial
changes to the display of the minimum spanning network.

Tabs

The right hand panel contains different tabs related to your data set of
choice.

Plot - The minimum spanning network itself

Data - A display of your data set

Command - The commands used to create the plot. You can copy
and paste this to an R file for reproducibility.

Save Plot - This provides a tool for you to save the plot to a
PDF or PNG image.

Session Information - displays the result of
sessionInfo for reproducibility.

Author(s)

Zhian N. Kamvar

See Also

Examples

## Not run: # Set up some datalibrary("poppr")library("magrittr")data(monpop)
splitStrata(monpop)<-~Tree/Year/Symptom
summary(monpop)
monpop_ssr <-c(CHMFc4 =7, CHMFc5 =2, CHMFc12 =4,
SEA =4, SED =4, SEE =2, SEG =6,
SEI =3, SEL =4, SEN =2, SEP =4,
SEQ =2, SER =4)
t26 <- monpop %>% setPop(~Tree)%>% popsub("26")%>% setPop(~Year/Symptom)
t26
imsn()# select Bruvo's distance and enter "monpop_ssr" into the Repeat Length field.# It is also possible to run this from github if you are connected to the internet.# This allows you to access any bug fixes that may have been updated before a formal# release on CRAN
shiny::runGitHub("grunwaldlab/poppr", subdir ="inst/shiny/msn_explorer")# You can also use your own distance matrices, but there's a small catch.# in order to do so, you must write a function that will subset the matrix# to whatever populations are in your data. Here's an example with the above# data set:
mondist <- bruvo.dist(monpop, replen = monpop_ssr)
myDist <-function(x, d = mondist){
dm <-as.matrix(d)# Convert the dist object to a square matrix
xi <- indNames(x)# Grab the sample names that existreturn(as.dist(dm[xi, xi]))# return only the elements that have the names# in the data set}# After executing imsn, choose:# Distance: custom# myDist
imsn()## End(Not run)